https://orcid.org/0000-0002-2797-8012
Figures
Abstract
The COVID-19 pandemic had a significant impact on mental health, straining an already overburdened healthcare system and highlighting health inequity issues. To streamline treatment efforts, targeting transdiagnostic risk factors for symptoms of emotional distress through a modular, transdiagnostic approach via telehealth may help to expand access to mental health treatment. Three transdiagnostic risk factors for emotional distress disorders that emerged as important treatment targets during the pandemic are anxiety sensitivity (AS) (i.e., fear of anxious arousal), intolerance of uncertainty (IU) (i.e., distress when confronted with uncertainty), and loneliness. To target AS, IU, and loneliness, we completed a pilot feasibility, acceptability, and utility trial of Coping Crew, our group, telehealth-delivered transdiagnostic treatment protocol. The 17 participants (Mage = 22.00, SD = 4.46; 71% female) rated the intervention and study protocol as feasible to deliver and acceptable and useful to address intervention targets. Evidence was mixed regarding feasibility, acceptability, and usefulness of the mobile app: 94% of the participants (n = 16) completed at least one daily survey 80% of the time, but only 35% of the participants (n = 6) completed at least 80% of the mobile app surveys over the course of the intervention. Most participants rated use of the app as acceptable and relevant to psychological improvements made due to the intervention. Exploratory analyses suggest potential reductions in transdiagnostic risk factors at post-intervention, which were maintained at 1- and 3-month follow-up. Detailed effect size estimates are provided. However, these should be interpreted with caution due to the small sample size and exploratory nature of the study.
Citation: Flynt S, Cho GY, Koscinski B, Accorso C, Knapp A, Gorka S, et al. (2025) A one-arm pilot trial of a telehealth CBT-based group intervention targeting transdiagnostic risk for emotional distress. PLoS One 20(6): e0303131. https://doi.org/10.1371/journal.pone.0303131
Editor: Cho Lee Wong, The Chinese University of Hong Kong, HONG KONG
Received: July 12, 2024; Accepted: April 14, 2025; Published: June 18, 2025
Copyright: © 2025 Flynt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Fully de-identified data are available through osf.io: https://osf.io/85dvz/.
Funding: The work of Dr. Knapp was supported by a K01 award through NIMH (MH121854). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: No authors have competing interests.
Introduction
The COVID-19 pandemic not only led to harmful physical health outcomes, but also increased negative mental health outcomes. Since the pandemic began, the percentage of people who reported their anxiety as ‘high’ or ‘extremely high’ has quadrupled from 5% to 20%, and the self-reported prevalence of depression has doubled [1]. In 2021, approximately two-thirds of individuals in the United States were experiencing moderate or severe psychological distress including elevated anxiety [2,3], depression [4], and loneliness [5]. Further, as the pandemic recedes, it is expected that people will continue to seek mental health services at a higher rate than before the pandemic [1,6]. Thus, there is an immediate need for brief, modular telehealth interventions targeting symptoms related to emotional distress. A flexible, transdiagnostic treatment approach may be optimal for addressing shared risk factors underlying emotional distress, particularly in response to the profound impact of this unprecedented environmental stressor. Anxiety sensitivity (AS), characterized by fear of anxious arousal; intolerance of uncertainty (IU), reflecting distress in uncertain situations; and loneliness are three key transdiagnostic risk factors exacerbated by the pandemic and strong predictors of emotional distress beyond its immediate effects.
Transdiagnostic risk factors
Emotional distress disorders encompass numerous mood and anxiety disorders that often share overlapping symptoms such as anxiety sensitivity and emotional avoidance [7,8]. Despite these shared symptom clusters and the commonality of comorbidity among mental disorders, disorder-specific cognitive-behavioral therapy (CBT) is widely used to treat emotional distress disorders. In a national survey of 43,093 adults receiving treatment for mental health conditions in the United States, researchers found that 80.3% had two or more diagnoses [9]. Treatments could be more efficient and effective if they focused on the core processes underlying multiple comorbid conditions [10]. Transdiagnostic risk factors have been implicated in the etiology of multiple disorders [11], and can directly influence the intensity of symptoms such and their development over time. Further, a growing body of literature demonstrates that not only can CBT reduce transdiagnostic risk factors, but reductions in these risk factors can lead to improvements in emotional distress disorder symptoms [12–15]. Consequently, using CBT to target transdiagnostic risk factors may have broad effects across multiple disorders. Three transdiagnostic risk factors with the capacity to reduce symptoms across emotional distress disorders include anxiety sensitivity, intolerance of uncertainty, and loneliness. We focus on these three risk factors due to their overarching influence and gained prominence due to the COVID-19 pandemic.
Anxiety sensitivity.
Anxiety sensitivity (AS) is the fear of bodily sensations associated with anxiety [16,17]. Lower-order dimensions of AS capture fears centered on physical, cognitive, and observable anxiety sensations and experiences. Elevated AS has been implicated as a risk factor for a wide range of maladaptive psychopathology, including anxiety disorders, depression, substance use and misuse, and suicidal urges [18–21]. Further, AS is positively associated with COVID-19-related worry, anxiety, functional impairment, and symptom severity [22] and predicts fear of COVID-19 [23] suggesting this will be an important elevated risk factor to target for future pandemics. Already, there is ample evidence that a brief AS intervention can reduce AS and associated psychopathology, including anxiety, depression, suicidal ideation, insomnia, and PTSD symptoms among others [14,18,24–26].
Intolerance of uncertainty.
Intolerance of uncertainty (IU) reflects “a dispositional characteristic that reflects an individual’s tendency to react negatively to uncertain situations, events, or outcomes, as well as the corresponding desire for certainty and predictability [27].” There is strong evidence of a higher-order IU factor and mixed evidence for two lower-order IU factors: prospective IU or discomfort when confronted with future uncertainty, and inhibitory IU or difficulty tolerating uncertainty in the moment [28,29]. IU is implicated in the onset of various psychiatric diagnoses, including generalized anxiety disorder (GAD [30]) and obsessive-compulsive disorder (OCD [31]). Changes in IU are related to increases in social anxiety, worry, and negative affect [32], along with changes in anxiety and depression symptoms across diagnostic categories [33]. Elevated IU during the COVID-19 pandemic has been identified as a risk factor for depression [5,34,35], fear and anxiety [36], and suicidal ideation [37]. IU is also positively associated with fear of COVID-19 [23], COVID-19 worry, sensitivity to COVID-19-related symptoms, and COVID-19-related interference in daily activities due to worry [37]. Already, there is emerging evidence that IU can be targeted in brief [38] and more intensive interventions [39,40].
Loneliness.
Loneliness is the subjective evaluation of one’s social relations as being inadequate or otherwise not fulfilling one’s social needs and desires [41–43]. Loneliness is conceptualized as a unidimensional construct [44] and has been linked to various psychiatric diagnoses and related behavioral risk factors including anxiety disorders, depression [5], and suicide attempts [45]. In a recent meta-analysis, loneliness was also associated with smoking, excessive drinking of alcohol, overeating, restricting food, as well as low levels of physical activity, paranoia, psychosis, elevated mental health symptom severity, poor recovery prognosis, poor interpersonal functioning, and increased mortality [43]. Loneliness has also been identified as a potential risk factor for depression symptom severity, psychological distress, and financial worries during COVID-19 [5]. Almost half (41%) of a Canadian sample indicated that social isolation has significantly negatively influenced their mental health [1]. Brief interventions targeting perceived burdensomeness and thwarted belongingness, two constructs reflecting biases in perceived social relations have provided preliminary evidence that loneliness could be reduced through a brief intervention [46,47]. Further, more intensive interventions have also proven successful in reducing loneliness [48].
Development of a modular CBT for transdiagnostic risk factors exacerbated by the COVID-19 pandemic
Modular CBT treatments use CBT principles (e.g., cognitive distortions) to target an individual process or set of related processes underlying psychopathology [49]. In this framework, risk processes such as AS, IU, and loneliness—can be assigned to a client depending on their specific needs [50]. Even in a group setting, a modular approach allows for personalization, as participants are guided to focus on the skills most relevant to how their particular transdiagnostic risk factors manfest themselves. Group discussions offer structure and support and individualized skills practice address participants’ unique concerns. Modular CBT treatments, with their flexible and adaptable nature, provide an ideal framework for designing interventions that effectively target and address transdiagnostic risk factors implicated in various emotional distress disorders [51].
A central premise of CBT is that treatment gains are consolidated through out-of-session skills practice by the client [52,53]. However, unlike in-session activities, these activities have traditionally occurred without direct support, which can reduce engagement and gains from between-session activities [52]. The ubiquitous nature of smartphones and the general comfort with mobile apps provides an excellent platform to increase between-session client engagement. Generalization of CBT skills to situations outside of the therapy room requires repeated rehearsal of treatment techniques when patients are experiencing real distress [54]. Ecological momentary assessment (EMA), delivered via mobile devices, offers an opportunity to easily implement skills practice as-needed by providing a monitoring system that can determine when skills practice might be most beneficial. By using EMA data, ecological momentary interventions (EMIs) provide a framework for ideal learning by providing the type of precise support in real time and in real world settings [55]. The use of a mobile app also allows for accountability of completing behavioral exercises, another central component of most CBT-based treatments. In addition to improving the intervention, EMA also reduces retrospective recall bias, increases ecological validity, and allows for examination of short-term temporal dynamics [56].
Current study
Brief, modular, transdiagnostic interventions based in empirically-supported CBT principles and delivered via a mobile app offer an opportunity to efficiently and effectively target several symptoms of emotional distress disorders exacerbated by the COVID-19 pandemic. AS, IU, and loneliness are three relevant risk factors to target given their associations with a wide range of psychopathology and distress due to the COVID-19 pandemic as well as evidence that they can be targeted through brief modules. We developed a 5-session group intervention, Coping Crew, using a modular framework and guided by CBT principles. A mobile app was used to track mood, provide brief cognitive nudges, and monitor interoceptive exposure (IE) and behavioral experiments (BEs) done as homework. The main purpose of the study was to examine the acceptability, feasibility, and utility of Coping Crew and the associated mobile app. Additionally, we explored preliminary effect size estimates and associations between transdiagnostic risk factors and emotional distress disorder symptom change over time.
Methods
Participants
Participants (N = 17) included 16 college students (94%) and one community member (6%) from a rural community in Southeast Ohio. Regarding self-reported race, 10 (59%) reported White, another two reported Asian and White (11.8%), another one reported Black (5.9%), and one reported another category (5.9%). All participants were non-Hispanic. Participants were 22 years old on average (standard deviation [SD] = 4.46), and 71% of participants were female.
Procedures
This study was supported by an internal grant provided to the last author and registered on clinicaltrials.gov (CT# NCT05019053). Participants were recruited through email, social media, and digital flyers to clients of the university psychology clinic. To be eligible, participants had to a) report either elevated AS (Anxiety Sensitivity Index-3 [ASI-3] ≥ 24), IU (Intolerance of Uncertainty Scale-12 [IUS-12] ≥ 35), or loneliness (NIH Toolbox Loneliness scale ≥ 13), b) own a smartphone, and c) have internet access. Exclusion criteria included current participation in other lab intervention studies (e.g., [57]), endorsement of psychotic features, or imminent risk for suicide. See Fig 1 for the Consort diagram reflecting study recruitment and retention. Out of 176 people who completed the screening, 158 met eligibility requirements. We contacted 96 participants sequentially from this list until we reached a sample of 20 participants. Because three participants withdrew after consenting, 17 total participants received the Coping Crew intervention. There was no single reason why participants withdrew: one participant did not want to download the mobile app, another participant expressed concerns related to their visa, and another did not reside in Ohio.
Potential participants first completed a screening survey online using Qualtrics. Eligible and interested participants completed a baseline virtual appointment consisting of a structured diagnostic interview and a battery of self-report measures. Following the baseline appointment, participants received Coping Crew, which consisted of five intervention sessions over six weeks. Next, participants completed a battery of self-report measures at post-intervention and 1- and 3-month follow-up. At post-intervention, participants completed a semi-structured interview containing questions designed to capture perceived acceptability, usefulness, and effectiveness of the intervention, in addition to the user experience of the accompanying mobile app. Participants were compensated up to $130 for their participation.
Development and structure of the Coping Crew intervention
Iteratively designed from focus groups and case studies, Coping Crew is a modular CBT intervention. We reviewed and adapted content from existing interventions for the anxiety sensitivity (AS) module (Computerized Anxiety Sensitivity Treatment [14,24,47]; and the intolerance of uncertainty (IU) module [38]. Principles from “A Therapist’s Guide to Brief Cognitive Behavioral Therapy [58]” served as a reference for developing modules and homework.
The intervention consisted of five weekly 60-minute group sessions delivered virtually via Microsoft Teams by two masters-level PhD graduate student clinicians (one male and one female) with CBT training. The first four sessions occurred weekly with a 2-week break between the fourth and fifth session to provide additional time for skills building. Groups ran one at a time from September 2021 to June 2022. Within each group, participants unable to attend a group session completed a make-up session with a clinician one-on-one before the next session.
Content of the Coping Crew intervention
Coping Crew is a modular CBT intervention that addresses emotional distress through a series of five remote group sessions. The program uses the modular CBT approach by focusing on a different topic each week: AS, IU, and loneliness. In all sessions, homework from the preceding week is reviewed in session with allocated time to address participant questions or concerns. The first session introduces the program, establishes group norms, and promotes voluntary intra-group sharing to enhance corrective feedback. Clinicians explain the CBT approach, and participants practice relaxation exercises.
The next three sessions each target a transdiagnostic risk factor for emotional distress using the same virtual group format. The second session introduces the role of anxiety as the body’s alarm system, and how to challenge misperceptions about anxiety sensations. Behavioral experiments (e.g., rapid breathing to mimic shortness of breath, or running in place to mimic a rapidly beating heart) are assigned to reinforce that anxiety sensations are benign. Session three introduces IU, its role in causing and maintaining anxiety, and common misperceptions about uncertainty. Participants are urged to challenge their own beliefs about uncertainty. Finally, participants design behavioral experiments that encourage confronting uncertainty. For example, in the pilot study, participants chose to delegate a task to a co-worker or answer a question in class they did not know the answer to with complete certainty. In session four, the focus shifts to social isolation experienced due to the pandemic and any accompanying loneliness. Strategies based on Behavioral Activation (BA) are introduced to encourage positive social interactions and challenge beliefs that inhibit interpersonal connection. Homework can include choosing social activities such as making small talk with a stranger or reaching out to an old friend.
The final session wraps up the intervention by revisiting all topics covered. In this session, clinicians summarize the information covered, encourage self-reflection, review strategies to maintain treatment gains, and discuss goal setting and values. Participants are provided with resources on how to obtain mental health care who to contact if experiencing a mental health crisis. This modular CBT-based intervention is designed to promote resilience in the face of anxiety, accompanying anxiety sensations, and social isolation.
Mobile intervention component
The mobile component of the intervention used a HIPAA-secured mobile app available from Metricwire (metricwire.com) named mEMA. Over a 12-hour window, a static survey was delivered and then three additional surveys were quasi-randomly delivered no closer than an hour and a half apart over the remaining time. Participants completed a set of self-report questionnaires in the morning and visual analog scales (VAS) across all assessments. Cognitive challenges were delivered if VAS ratings were ≥ 5 on a 10-point scale. These cognitive challenges focused on “key takeaways” from each week’s intervention topic collaboratively developed by members of each group beginning in week two. Each group cohort generated their own “key takeaways” that challenged maladaptive cognitions related to each session’s content. For example, AS-specific challenges included “anxiety is just a feeling” and “the anxiety I feel now will pass.” IU-specific challenges included “even if things go off plan, I can adjust for that” and “uncertainty doesn’t mean something bad is going to happen” and loneliness-specific challenges included “avoiding social interactions won’t improve feeling alone.” After week two, whenever a participant reported anxiety or stress ≥ 5, one of these key takeaways was delivered randomly from a list of takeaways generated in-session. AS and IU-related key takeaways were randomly selected from after week three with a 75% likelihood of drawing an IU-related key takeaway. After week four, AS and IU key takeaways were equally delivered whenever a participant reported elevated anxiety or stress, and loneliness key takeaways were delivered whenever a participant reported elevated depression. The prompts for each emotional distress and the corresponding behavioral experiments evolved with the progression of the intervention, giving participants an interactive, personalized, and comprehensive experience.
Feasibility, acceptability, and utility measures
The study employs a custom set of evaluative measures to gauge the feasibility of the intervention and associated assessment battery, and acceptability and utility of the intervention and mobile app. Feasibility was determined by attendance rates for assessment and intervention sessions, and rates of survey completion via mobile app. Acceptability was measured by perceived acceptability of the intervention and acceptability of the mobile app, measured by participants’ attitudes toward the intervention’s impact on AS, IU, loneliness, anxiety, stress, and depression, as well as the anticipated sustainability of clinical improvements. Perceived utility of the services provided included questions focused on the ability of the intervention to reduce anxiety, stress, and loneliness and use of the mobile app to track weekly activities. All items were assessed on a 5-point Likert-like scale with higher scores indicating greater agreement with the item.
Assessment measures
Anxiety Sensitivity Index-3.
The ASI-3 [59] is an 18-item measure that assesses fear of physical, cognitive, and observable anxiety sensations by measuring three AS subfactors (physical, cognitive, and social concerns). Items are assessed on a 5-point Likert-like scale with higher scores indicating greater AS. In the current study, reliability for the ASI-3 from baseline to follow-up was excellent (Cronbach’s alpha [α] =.91−.95).
Intolerance of Uncertainty Scale short form.
The IUS-12 is a 12-item measure that assesses the degree to which individuals can tolerate the uncertainty of ambiguous situations, cognitive and behavioral responses to uncertainty, perceived implications of uncertainty, and attempts to control the future through two factors: prospective anxiety and inhibitory anxiety [60]. Items are assessed on a 5-point Likert-like scale where 1 indicates that the respondent does not find the statement to be characteristic of themselves, and 5 indicates that the respondent finds the statement to be very characteristic of themselves. In the current study, reliability for the IUS-12 from baseline to 1-week follow-up was good to excellent (α = .83−.93).
NIH Toolbox: Adult Social Relationship Scales (Loneliness).
The National Institute of Health (NIH) Toolbox Loneliness Scale [61] is 5-item measure within the NIH Toolbox Adult Social Relationship Scales that assesses loneliness, conceptualized as the perception that one is lonely or socially isolated from others. Items are assessed on a 5-point scale with 1 indicating the respondent never has the experience, and 5 indicating that the respondent always has the experience. In the current study, reliability for the NIH Loneliness scale from baseline to 2-week follow-up was good to excellent (α = .87−.96).
PROMIS Negative Affect Scales: Anxiety & depression.
The Patient Reported Outcomes Measurement Information System (PROMIS) Negative Affect scales include measures for assessing anxiety and depression [62]. The PROMIS Anxiety item bank consists of 29 items, and the PROMIS Depression item bank consists of 28 items. We utilized the short forms of both measures, containing 8 items each. Reliability for the PROMIS Depression scale from baseline to 3-month follow-up was excellent (α = .95−.97).
COVID Impact Battery: COVID-19 Worry Scale.
The COVID Impact Battery (CIB) captures the influence of the COVID-19 pandemic on individual mental health across three COVID-19-related scales: behavior, worry, and disability [63]. The COVID-19 Worry scale is an 11-item measure that assesses COVID-related stress, associated with avoidance of infection via safety behaviors, self-checking, and reassurance-seeking (i.e., utilization of the healthcare system), as well as worry about socioeconomic impacts of the pandemic [64]. Items are assessed on 0–4 scales with higher scores capturing increased worry. In the current study, reliability for the CIB Worry scale from baseline to 3-month follow-up was good to excellent (α = .84−.92).
EMA measures
During the intervention, participants completed four daily brief surveys. The first survey was sent at the beginning of a 12-hour period predetermined by the client and clinician, followed by three surveys delivered randomly throughout the remaining 12-hour window. All surveys were delivered at least 90 minutes apart. The purpose of the personalized time period was to maximize participant engagement, by increasing likelihood of survey completion via convenience. The brief surveys were designed to take approximately one minute to complete. EMA measures included the CIB and four Visual Analog Scales (VAS): Anxiety, Depression, Stress, and Loneliness. VAS measures were formatted as slider bars for participants to rate their levels on a 1–100 scale (e.g., How anxious are you now?) with higher scores indicating more severe levels of the rated construct.
Data analytic plan
This study was a one-arm pilot acceptability and feasibility study, registered on clinicaltrials.gov (NCT#05019053). Reporting was done in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines, adopted for one-arm trials [65]. The main outcomes of the study, acceptability, feasibility, and utility ratings, were first calculated by examining mean item-level scores across several measures modified to capture participant ratings of acceptability, feasibility, and utility. Acceptability and feasibility were also assessed through monitoring recruitment, retention, and use of the mobile app.
Exploratory analyses, conducted as repeated measures ANOVA, examined changed in mean AS, IU, loneliness, anxiety, depression, and worry related to the COVID-19 pandemic from baseline to post-intervention and 1-month follow-up (see S1 Table). Mauchly’s test was used to determine if the assumption of sphericity was met. If it was not met, the Greenhouse Geisser statistic was used to determine the significance of the tests of within-subjects effects. Because this trial was not designed to be powered to find all but the largest of effect sizes, we were primarily interested in standardized effect size estimates (dw) which was calculated as the change in scores divided by the pooled baseline standard deviation.
Results
Eligibility criteria met for enrollment
Eligibility criteria included elevations of at least 1 SD above the mean on at least one risk factor: AS, IU, or loneliness. At baseline, 13 participants had elevated ASI-3 scores, 17 participants had elevated IUS-12 scores, and6 participants had elevated NIH Loneliness scores.
Acceptability, feasibility, and utility
Acceptability, feasibility, and utility were examined across attendance and performance metrics as well as response to Likert-like questions. Out of 20 participants who consented and completed the baseline session, 3participants withdrew leaving 17 participants (85%) who received Coping Crew. All 17 participants attended one or more treatment sessions, and 15 attended all five intervention sessions. All 17 participants completed their baseline and 1-month follow-up survey batteries. Only one participant did not complete the 3-month follow-up battery. Regarding missing sessions, zero participants missed more than one session and lack of attendance varied by session number (session two = 2 participants; session three = 1 participant; session four = 1 participant). This suggests that the deliverability of the intervention and corresponding assessment protocol are feasible. Performance on the daily surveys via the mobile app was mixed. Only 6 (35%) of 17 participants completed 80% or more of the surveys delivered to their mobile app. However, 16 of 17 (94%) completed at least one survey daily 80% of the days. Participants completed an average of 13 (range = 2–23) behavioral experiments assigned as daily homework.
Participants rated perceived efficacy of the intervention component delivered by clinicians across 10 items on a scale of 1 = “strongly disagreed with the item” to 5 = “strongly agreed” (see Top Panel of Table 1). The average item rating was 4.23 (range = 3.75–4.63), reflecting that on average, participants agreed on acceptability of the intervention for anxiety, stress, and depression. Of note, participants strongly agreed (M = 4.63, SD = .62) that they would recommend this approach to others who are lonely and anxious/stressed.
Participants rated perceived efficacy of the mobile app components of the intervention across 8 items using the same 1–5 Likert scale (see Middle Panel of Table 1). The average rating was 3.77 (range = 2.69–4.44), indicating participants found the mobile app acceptable in a variety of ways, including finding the daily surveys useful (M = 3.88, SD = .89), and using their cell phone to complete homework (M = 4.25, SD = .93). Participants strongly agreed (M = 4.44, SD = .81) that the mobile app worked well. Of note, the average level of agreement on whether the mobile app was helpful for loneliness was neutral, meaning participants neither agreed or disagreed that it was helpful.
Missing data analysis
We examined differences in baseline mean scores between participants who were missing data versus participants who were not missing any data. A significant difference in PROMIS Depression scale scores was found, t(15) = −2.06, p = .03. Participants who provided data at all assessment periods had lower depression scores (M = 15.50, SD = 5.66; n = 8) than did participants who did not provide data at all assessment periods (M = 21.56, SD = 6.39; n = 9). No other differences were found.
Baseline descriptive statistics and correlations
Table 2 contains correlations between baseline variables as well as sample means. ASI-3, IUS-12, and NIH Loneliness scores were significantly correlated (rs = .51−.72, ps < .05). All three risk factors were associated with the PROMIS Anxiety scale (rs = .55−.79, ps < .05). The NIH Loneliness scale was also associated with scores on the PROMIS Depression scale (r = .64, p < .05) and COVID-19 Worry scale (r = .50, p < .05).
Exploratory analyses: Repeated measures ANOVAs
Exploratory analyses were conducted to assess changes in transdiagnostic risk factors and emotional distress symptoms from baseline to follow-up. Preliminary reductions in AS, IU, and loneliness were observed from baseline to follow-up. The results suggest medium-to-large effect size reductions, though they should be interpreted with caution due to the small sample size. Detailed results of the repeated measures ANOVA, including effect sizes, are presented in S1 Table.
Discussion
This study was a preliminary feasibility, acceptability, and utility one armed pilot trial for Coping Crew, a modular group CBT-based transdiagnostic intervention. To encourage use of in-session skills developed, we designed a mobile app to supplement the treatment. The intervention and assessment battery were feasible, acceptable, and had utility in addressing the targeted transdiagnostic risk factors. Although the mobile app was rated as acceptable and had utility in aiding delivery of the intervention, rates of completion were lower than we had proposed as our threshold for acceptability.
We determined that Coping Crew was acceptable and feasible based on recruitment and retention rates as well as ratings on measures assessing agreement that aspects of the intervention were acceptable and useful. We retained 85% of participants (17 of 20) consented to receive the intervention, and 88% (15 of 17) of participants attended all five intervention sessions as well as the 3-month follow-up and 94% (16) attended the 1-month follow-up. On average, the intervention content was rated as acceptable. Importantly, on average, participants strongly agreed that they would recommend this intervention to others who are lonely and anxious/stressed. This suggests that client perceptions of Coping Crew align with the intended purpose of the treatment.
In contrast to the almost universally positive perceptions of the telehealth component, perceptions about the mobile app were more mixed. We hypothesized, a priori, that if 80% of the participants completed at least 80% of the surveys delivered to their phone, we would consider that as evidence that the mobile app was a feasible accompaniment to the intervention. Only six participants (35%) met this threshold. However, 94% of participants completed at least one survey on more than 80% of the days. Evidence for the acceptability and utility of the mobile app was mixed. On average, participants agreed they completed an acceptable number of surveys daily, that they benefitted from using their phone to complete their homework, and that the prompts they received regarding anxiety, uncertainty, and loneliness were helpful. We did learn from responses to open- and close-ended questions that one participant was opposed to use of the mobile app. This participant disagreed to strongly disagreed that the mobile app to track their mood was a useful adjunct to the treatment. It is possible there may be barriers to engaging with mobile apps for some participants. For example, older adults, low-income groups, and rural populations may not have the necessary resources or digital literacy skills to benefit from an app [66]. Others may be concerned about data privacy and confidentiality [67]. There is an emerging literature on engagement efforts for mHealth interventions; more is needed to understand which factors may limit research participants or clinical patient from benefitting from mobile app interventions and supplements and whether acceptable substitutes exist.
Detailed statistical results, including effect sizes, are available in the supplementary material to support transparency and future meta-analyses. Effect sizes obtained in one-armed pilot trials must be interpreted with caution due to the lack of a control condition. Further, our small sample size also precludes estimations of treatment effects. Preliminary analyses suggest that reductions in AS, IU, and loneliness were maintained at follow-up assessments, supporting the potential for targeting these transdiagnostic risk factors in future, more rigorous trials. However, no significant reductions were observed in depression symptoms, which may reflect limitations of the intervention or sample characteristics.
A further benefit of validating Coping Crew via a SMART trial is the ability to test the inherent modularity of the intervention. Modular treatments have emerged as a promising approach that bridges the gap between traditional psychotherapy practices, driven by clinician intuition, and manualized treatments, characterized by rigid protocols [68]. Whereas traditional approaches rely on clinicians’ adaptability to meet clients’ evolving needs, manualized treatments provide structure at the cost of flexibility. In contrast, modular treatments allow for manualized treatments to be utilized based solely on participants’ current needs [68]. Modular frameworks acknowledge the finite nature of therapeutic tools and offer the potential for “continuous scaling” from a common treatment framework [68]. For instance, in the development of Coping Crew, an intervention currently under development, principles derived from both first and third-wave cognitive-behavioral therapy (CBT) as well as mindfulness practices are utilized to explore the intricate interplay among thoughts, feelings, and behaviors, with a specific focus on the psychological modifiability of thoughts and behaviors [69].
Limitations
Despite grounding our study in prior literature, several limitations should be acknowledged. First, reliance on self-report measures introduces potential biases associated with memory processes and response tendencies. Whereas real-time data collection via EMA may mitigate some biases, further exploration of alternative measurement approaches is warranted. Second, the small sample size and convenience sampling resulting in a primarily White non-Hispanic and female sample limit the generalizability of our findings. Further, the lack of a control group limits interpretation of change. Multi-arm fully powered RCT designs are needed to fully explore efficacy of Coping Crew as well as to identify mechanisms underlying treatment effects. These limitations provide directions for future research and further highlight the need for an RCT comparing Coping Crew to a well-validated control condition.
Conclusion
This study served as a one-arm pilot feasibility, acceptability, and utility trial. Results were promising for the intervention and assessment protocol. Considerations for next steps include how to increase engagement in the mobile app and fitting intervention modules to sample characteristics. These findings leave us eager to conduct next stages in this modular transdiagnostic intervention that includes a mobile app component to supplement the intervention with limited clinical effort.
Supporting information
S1 Table. Detailed effect-size estimates for all outcomes.
https://doi.org/10.1371/journal.pone.0303131.s001
(DOCX)
S1 File. CONSORT checklist pilot trial corrected.
https://doi.org/10.1371/journal.pone.0303131.s002
(DOCX)
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